Paper Info
Title
DEEP MULTIWAY CANONICAL CORRELATION ANALYSIS FOR MULTI-SUBJECT EEG NORMALIZATION
Abstract
The normalization of brain recordings from multiple subjects responding to the natural stimuli is one of the key challenges in auditory neuroscience. The objective of this normalization is to transform the brain data in such a way as to remove the inter-subject redundancies and to boost the component related to the stimuli. In this paper, we propose a deep learning framework to improve the correlation of electroencephalography (EEG) data recorded from multiple subjects engaged in an audio listening task. The proposed model extends the linear multi-way canonical correlation analysis (CCA) for audio-EEG analysis using an auto-encoder network with a shared encoder layer. The model is trained to optimize a combined loss involving correlation and reconstruction. The experiments are performed on EEG data collected from subjects listening to natural speech and music. In these experiments, we show that the proposed deep multiway CCA (DMCCA) based model significantly improves the correlations over the linear multi-way CCA approach with absolute improvements of 0:08 and 0:29 in terms of the Pearson correlation values for speech and music tasks respectively.
Year
DOI
Venue
2021
10.1109/ICASSP39728.2021.9414274
2021 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP 2021)
Keywords
DocType
Citations 
Canonical correlation analysis (CCA), multiway CCA, Deep CCA, Audio-EEG analysis
Conference
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Jaswanth Reddy Katthi100.34
Sriram Ganapathy225239.62